Module markov.api.schemas.model_recording
Classes
class EvaluationRecording (name: str,
model_id: str,
note: str = '',
dataset_id: Optional[str] = '',
info: dict = <factory>)-
The data model to store metadata for evaluations
Static methods
def create_from_dict(value: dict) ‑> EvaluationRecording
-
Create this object from the serialized (dictionary) representation of this object where the key's are attributes and values are attribute values.
Args
value
:Dict
- Dictionary serialized value of this object.
Returns
EvaluationRecording
def create_from_json(value: str) ‑> EvaluationRecording
-
Create EvaluationRecording object from serialized JSON.
Args
value
:str
- JSON string that is serialized representation of this object.
Returns
EvaluationRecording object
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var dataset_id : str | None
-
Misc information we need to save about this recording with handlers. it's key->value format
var info : dict
var model_id : str
-
details about this evaluation run that might be relevant for the
var name : str
-
The id of the model which this evaluation belongs to
var note : str
-
The dataset id used for this evaluation
Methods
def get_dict(self) ‑> dict
def get_json(self) ‑> str
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str
class ModelRecordingConfig (name: str,
user_model_id: str,
user_data_id: str,
model_name: str,
model_class: str,
note: str = '',
info: dict = <factory>)-
Configuration for Model Recording
Static methods
def create_from_dict(value: dict) ‑> ModelRecordingConfig
-
Create this object from the serialized (dictionary) representation of this object where the key's are attributes and values are attribute values.
Args
value
:Dict
- Dictionary serialized value of this object.
Returns
ModelRecordingConfig
def create_from_json(value: str) ‑> ModelRecordingConfig
-
Create ModelRecordingConfig object from serialized JSON.
Args
value
:str
- JSON string that is serialized representation of this object.
Returns
ModelRecordConfig object
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var info : dict
var model_class : str
-
details about this model run that might be relevant for the
var model_name : str
-
Defines the model class (Regression/ Classification/ Ranking etc)
var name : str
-
Identifier provided by the user to identify their model
var note : str
-
Misc information we need to save about this recording with handlers. it's key->value format
var user_data_id : str
-
Human readable name of the model
var user_model_id : str
-
Unique identifier associated with the dataset that has gathered model inference/ground truth results against
Methods
def get_dict(self) ‑> dict
def get_json(self) ‑> str
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str
class MultiTagInferenceRecord (urid: str,
meta_data: Optional[List[RecordMetaData]] = <factory>,
custom_metrics: Optional[List[RecordCustomMetric]] = <factory>,
mir_list: List[SingleTagInferenceRecord] = <factory>)-
Useful for tagging Inference records where there is a single object (such as an image) and within that image there are multiple tags (objects recognized). MultiTagInferenceRecord is a collection of ModelInferenceRecords, where each inference record captures the actual/predicted/score of each tag.
Class variables
var matches
Static methods
def create_from_dict(value: dict) ‑> MultiTagInferenceRecord
-
Deserialize the dictionary representation of this object back to an InferenceRecord.
Args
value
:dict
- Dict representation of this record.
Returns
InferenceRecord Instance.
def create_from_json(value: str) ‑> MultiTagInferenceRecord
-
Deserialize the json representation of this object back to an InferenceRecord.
Args
value
:str
- JSON representation of this record
Returns
InferenceRecord Instance
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var custom_metrics : List[RecordCustomMetric] | None
var meta_data : List[RecordMetaData] | None
var mir_list : List[SingleTagInferenceRecord]
var urid : str
-
meta data_set (optional) to be associated with this inference record. Meta data_set is any additional information that user wants to provide to help them in their analysis.
Methods
def add_evaluation(self, actual: Any, predicted: Any, score: float)
-
Add a tag evaluation to this Mult-Tag inference record for storing/computing the metrics
Args
actual
:Any
- actual ground truth tag associated
predicted
:Any
- Predicted tag value
score
:float
- confidence of prediction or probability score of prediction
Returns
void
def add_meta_data(self,
label: str,
value: Any,
meta_type: RecordMetaType)-
Args
label
:str
- associated label for a metadata to be associated with the record.
- MetadataType defines type of information sent so that Frontend can render it correctly
value
:str
- value associated with this
meta_type
:RecordMetaType
- supported meta_types for our FE to render the meta_type correctly.
Returns
void
def add_record_meta_data(self,
rec_md: RecordMetaData)-
Add the instance of RecordMetaData directly to the InferenceRecord
Args
rec_md
:RecordMetaData
- RecordMetaData object captures the key-value relationship and the meta_data
type information
Returns
void
def compute_tag_ratio(self) ‑> RecordCustomMetric
def get_dict(self) ‑> Dict
-
Serialize this object to a dictionary.
Returns
Dictionary of this object.
def get_json(self) ‑> str
-
Serialize this object to JSON
Returns
JSON string of this object
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str
class RecordCustomMetric (label: str, value: Union[float, int])
-
Custom Metric that user can associate with the record for Markov to compute custom statistics
Static methods
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var label : str
var value : float | int
Methods
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str
class RecordMetaData (label: str,
value: Union[Dict, str],
meta_type: RecordMetaType)-
RecordMetaData contains additional information that user wants to send with each record for MarkovML to register. The information can be one of the RecordMetaType ImageUrl: pointing to an ImageURL hosted at your server or S3. Please make sure CORS Is disabled if you want us to render it the information. Text: Free form text
URI: A URI associated with data_set to href it back
Static methods
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var label : str
-
value of this metadata
var meta_type : RecordMetaType
var value : Dict | str
-
Type of this meta data_set
Methods
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str
class RecordMetaType (value, names=None, *, module=None, qualname=None, type=None, start=1)
-
Supported MetaData type
Ancestors
- enum.Enum
Class variables
var Histogram : Final
var ImageUrl : Final
-
This will be on FE as a clickable URI
var Text : Final
-
Represents an imageURL it will be rendered on FE. Please make sure there is no CORS restriction.
var Uri : Final
-
Plot this data as a histogram
class SingleTagInferenceRecord (inferred: Any,
actual: Any,
score: float,
urid: str,
meta_data: Optional[List[RecordMetaData]] = <factory>,
custom_metrics: Optional[List[RecordCustomMetric]] = <factory>)-
Inference record converts user data_set into a recording format that MarkovML can understand.
Static methods
def create_from_dict(value: dict) ‑> SingleTagInferenceRecord
-
Deserialize the dictionary representation of this object back to an InferenceRecord.
Args
value
:dict
- Dict representation of this record.
Returns
InferenceRecord Instance.
def create_from_json(value: str) ‑> SingleTagInferenceRecord
-
Deserialize the json representation of this object back to an InferenceRecord.
Args
value
:str
- JSON representation of this record
Returns
InferenceRecord Instance
def from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) ‑> ~A
def from_json(s: str | bytes | bytearray,
*,
parse_float=None,
parse_int=None,
parse_constant=None,
infer_missing=False,
**kw) ‑> ~Adef schema(*,
infer_missing: bool = False,
only=None,
exclude=(),
many: bool = False,
context=None,
load_only=(),
dump_only=(),
partial: bool = False,
unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Instance variables
var actual : Any
-
Score for this prediction
var custom_metrics : List[RecordCustomMetric] | None
var inferred : Any
-
The actual value or the ground truth value of this model
var meta_data : List[RecordMetaData] | None
var score : float
-
User maintained record id that reference back this particular record back to their dataset for analysis.
var urid : str
-
meta data_set (optional) to be associated with this inference record. Meta data_set is any additional information that user wants to provide to help them in their analysis.
Methods
def add_custom_metric(self, label: str, value: Union[int, float])
-
Custom metrics help users evaluate models based on external business metrics they care about. For example, if the user wants to measure how many times a tweet identified as "fun" had a "dog" in it. They can create a custom metric with name "dog_fun_count" and value as number of times "dog" appeared in a tweet.
Args
label
:str
- Name of the custom metric that you need to track for this evaluation
value
:Union[Float,Int]
- value associated with the label. It has to be numeric.
Returns:
def add_meta_data_instance(self,
key: str,
value: Any,
meta_type: RecordMetaType)-
Add a single meta_data instance with key as a string type and value (any)
Args
key
:str
- name or the key of the meta_data field you want to store
value
:Any
- value associated with the key
meta_type
:RecordMetaType
- type associated with the meta data instance we are adding.
Returns
None
def add_record_meta_data(self,
rec_md: RecordMetaData)def get_dict(self) ‑> dict
-
Serialize this object to a dictionary.
Returns
Dictionary of this object.
def get_json(self) ‑> str
-
Serialize this object to JSON
Returns
JSON string of this object
def to_dict(self, encode_json=False) ‑> Dict[str, dict | list | str | int | float | bool | None]
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: int | str | None = None,
separators: Tuple[str, str] | None = None,
default: Callable | None = None,
sort_keys: bool = False,
**kw) ‑> str